The fundamental observable in Global Navigation Satellite System Reflectometry (GNSS-R) is the so-called Delay-Doppler Map (DDM), which is derived from the cross-correlation of the reflected signal with either the direct signal or a locally generated replica of the transmitted signal as implemented in our algorithm. Efficient coherent integration is essential for enhancing the signal-to-noise ratio (SNR) and improving the quality of the retrieved data products. This work investigates the limitations of coherent integration in conventional GNSS-R (cGNSS-R) and proposes a novel blind compensation technique to extend integration time by mitigating phase differences caused by navigation bit changes and propagation effects. The maximum length of coherent integration depends on the application; however, achieving a higher SNR within the same integration time enhances the quality of DDMs. Simulation results obtained from high-dynamic synthetic data demonstrate the effectiveness of the proposed technique. Additionally, real data from the UK-DMC reflectometry mission is used to validate the approach.

This study presents the implementation of an onboard Image Classification Unit (ICU) for the AlAinSat-1 CubeSat, aiming to enhance its autonomy and data processing capabilities. The focus is on integrating a trained CNN model onto AlAinSat-1’s STM32 microcontroller.The designed system employs TensorFlow models trained for image classification tasks relevant to CubeSat missions, such as target accuracy detection, to determine if the image captures the required target, and image quality assessment to estimate cloud cover percentage.The integration process onto the STM32 microcontroller involves addressing the resource constraints inherent in CubeSat platforms. The paper details the optimization techniques applied to adapt the model to the STM32 architecture, ensuring efficient execution within the available hardware resources.Key aspects covered in the study include hardware-software co-design considerations, addressing memory and computational limitations, and optimizing mission duration for power consumption efficiency. Additionally, the development of a reliable communication interface between the onboard Image Classification Unit (ICU) and CubeSat’s main control system is discussed to facilitate seamless integration into the overall satellite architecture.The presented implementation enables CubeSats to perform onboard image classification tasks, reducing the need for constant communication with ground stations and enabling quicker response times for mission decisions. This research contributes to the growing field of embedded machine learning applications in spaceborne remote sensing systems, showcasing the feasibility and benefits of incorporating image processing capabilities on resource-constrained platforms.

Radars have been implemented using Software Defined Radios (SDR) for more than a decade. SDRs are convenient in terms of hardware development since the signal generation, conditioning, and post-processing can easily be implemented in hardware. However, signal synchronization remains a major issue, and it can seriously impact the final performance. In this study, the synchronization and the calibration of the transmitted signals in an SDR-based radar transmitting PRN modulated pulse is studied. Time synchronization and jitter are compensated using a "Filter Delay" block, and a peak detector in a reference channel. The amplitude and phase of the peak of the cross-correlation of a sample of the transmitted signal is used to calibrate the received echoes allowing to compensate on a pulse-by-pulse basis the amplitude and phase drifts.

Earth Observation (EO) using Signals of Opportunity (SoOp), the advent of high-performance and customizable Software Defined Radios (SDRs), and small satellites are revolutionizing today many Remote Sensing techniques. One of the most widely used SoOp are the Global Navigation Satellite System signals used for Radio Occultations (GNSS-RO) and Reflectometry (GNSS-R). In this last one, the reflected GNSS signals acquire properties of the surface where they are reflected, and when these signals are compared to the direct ones, one can infer surface roughness and dielectric constant information. Nowadays SDR-based GNSS-R instruments are becoming more cost-effective, power-efficient, and small enough to be adopted as CubeSats payloads. In GNSS-R receivers, all the information resides in the so-called Delay Doppler Map (DDM), which is the cross-correlation between the reflected signal and the direct one (interferometric GNSS-R or iGNSS-R), or a locally generated replica of the direct one (conventional GNSS-R or cGNSS-R) for different delays and Doppler frequency cuts. Producing these DDMs is computationally intensive due to the large number of Fast Fourier Transforms (FFTs) involved. To develop an efficient GNSS-R instrument, this work presents the development of such an instrument— the first of its kind in the United Arab Emirates (UAE)— and introduces its first operational version. Also, it explores the capture of raw GPS L1 signal, and its processing, having as auxiliary or reference information of the PRN codes of the satellites in view, and their central Doppler frequencies. The coherent and incoherent integration times will also be traded off to enhance the spatial resolution.

Climate change has been affecting human life since more than a decade ago. IEEE GRSS through IEEE GRSS Student Grand Challenge program has gathered students from several universities including Telkom University and Kyushu Institute of Technology to develop a CubeSat payload for climate change monitoring mission. Both teams were working on the development of the experimental remote sensing payload that is integrated with an on-board classification system. The payload is equipped with a small serial camera and two microcontrollers for controlling and for applying the classification algorithm. The ultimate target of this payload is detecting cloud coverage in the images. It is an indication of environmental change. This project has yielded a payload called Locana payload. It brings an Arducam OV5642 and two microcontrollers ATSAMD21 and SMT32F7 as the camera controller and cloud classification processor consecutively. This project has given a priceless educational experience for both teams, they were separated geographically but they were working on the same PCB board. The hardware and software design and integration have been carried out utilizing online meetings and remote access due to the pandemic.

AlainSat-1 is an educational and scientific nanosatellite project that was initiated in late 2019 by the IEEE Geoscience and Remote Sensing Society (GRSS) along with National Space Science and Technology Center (NSSTC) of UAE University in the frame of the 2nd Student Grand Challenge [1]. The project involves close collaboration between four international universities to design, build, test and launch a remote sensing CubeSat.The spacecraft is a 3U CubeSat that has a mass of around 4 Kgs. The spacecraft has an active 3-axis control system capable of attitude determination and control to less than one degree. Two communications systems will be used on-board: a UHF System and an S-Band System. The project has passed the Critical Design Review (CDR) stage and is currently in the assembly and integration phase. The satellite is currently planned for launch to a sun-synchronous orbit on-board a Falcon 9 rocket in the second quarter of 2024.

Climate change has occurred as a result of human activities. It can trigger unexpected disasters such as floods or drought. Further severe events may be avoided by monitoring climate change. A method to do that is monitoring the cloud coverage in some areas. In this paper, the development of a CubeSat payload that can monitor cloud coverage is presented. It contains a COTS camera module, microcontrollers, and a cloud classification algorithm. This payload is a joint research between Telkom University and Kyushu Institute of Technology under IEEE GRSS 2nd Student Grand Challenge. This payload has been implemented and tested and the result is the payload able to capture images in a long period and classify the cloud feature of each of them. Currently, it has reached the flight model stage and is ready to get further space environmental tests.

One of the most reliable and effective options to approach disaster-affected areas or remote places is to utilize the satellite-based solutions. The store and forward (SFward) mission is developed to leverage this concept. In BIRDS-4 SFward mission, the satellites collect data from ground sensor terminals that contain specific data needed by an application and store it to an internal memory. When one of the satellites passes over the main ground station, it forwards the stored data. This mission can also be used to broadcast announcements from ground centers to distributed receivers from the amateur community. This is most useful during disasters where conventional terrestrial networks are down. The mission is useful for transmitting data from remote areas without ground-based networks, allowing the collection of data from these remote sites. This can be utilized to achieve many applications.

Although the cost of development is cheap, cube satellites are limited in power, size, and downlink capabilities. By optimizing algorithms and the hardware these algorithms run, one overcomes these limitations, thus, allowing more missions to run and more data to be collected from it. Images, for example, are relatively big in size and if the satellite were able to know which image data to downlink, it could save a lot of time and resources. For this purpose, a cloud detection algorithm based on the U-net architecture was developed using the TensorFlow library. This model will be trained using a dataset of 15,263 images taken from the Landsat 8 satellite while the SPARCS cloud assessment dataset was used to evaluate the model on images it was not trained on. To limit the size of the input data, only the RGB band was used. After optimizing the model's parameters, the model shows that it achieved an overall accuracy of ∼85%. Furthermore, testing of the same model on images of lower resolution taken from CubeSats showed that it still was fairly accurate and would manage to work in most CubeSats that would only be able to take low resolution images. The model was then quantized and was then converted to a C code 8 bytes array using the TensorFlow Lite library to reduce its size and operation. It is then implemented inside a STM32F746BGT6 microcontroller which can then be used by cube satellites to detect clouds from the images it would take. This module is the Image Classification Unit (ICU). As a proof of concept, this ICU will be implemented inside a 3U CubeSats mission developed at the National Space Science and Technology Center, UAE.